Data Science: Extented Responses

Explain the importance of securely collecting, storing, and manipulating data in enterprise systems.

• Secure data practices protect sensitive information and maintain customer trust.

• Data security is only important for small businesses, not enterprises.

• Enterprises do not need to comply with legal requirements regarding data.

• Data manipulation is irrelevant to data accuracy and analysis.

Evaluate how the use of innovative technologies, such as AI and machine learning, has improved enterprise computing systems.

• Innovative technologies like AI and machine learning have transformed enterprise computing by automating processes and improving accuracy.

• The use of AI and machine learning has led to a complete replacement of human workers in enterprise computing.

• AI and machine learning have made enterprise computing systems more complex and less user-friendly.

• Innovative technologies have no significant impact on enterprise computing systems.

Explain how enterprise systems can be designed to meet the needs of different audiences.

• Enterprise systems should only focus on data security and not user experience.

• Enterprise systems must be user-friendly, accessible, and customisable to meet different user needs.

• Enterprise systems should be designed solely for IT professionals and not for end-users.

• Enterprise systems can be effective if they are complex and require extensive training to use.

Justify the use of data visualisation tools in the development of enterprise computing systems.

• They make complex data easier to understand and support faster decision-making.

• They are primarily used for data storage and management.

• They replace the need for data analysis entirely.

• They are only useful for technical staff in interpreting data.

Analyse the role of data in supporting decisions in an enterprise computing system.

• Data is irrelevant in decision-making processes.

• Data helps identify trends and test solutions for informed decision-making.

• Data only serves to store information without any analytical value.

• Data is primarily used for marketing purposes only.

Evaluate the effectiveness of using cloud computing for storing enterprise data.

• Cloud computing is effective due to its scalability, cost-efficiency, and accessibility.

• Cloud computing is ineffective because it requires expensive infrastructure.

• Cloud computing is only useful for small businesses, not enterprises.

• Cloud computing does not allow for remote access, limiting collaboration.

Discuss how enterprise computing systems meet the needs of different types of enterprises through data science applications.

• Enterprise computing systems only support large corporations and are not useful for small businesses.

• Data science applications in enterprise computing systems are limited to financial analysis and do not include customer data.

• Enterprise computing systems support different types of enterprises by collecting, analysing, and using data to improve operations.

• Data science applications are primarily used for marketing purposes and do not impact operational efficiency.

Explain how social, ethical, and legal issues affect the design of enterprise systems using data science.

• Enterprise systems must prioritize user interface design over legal compliance.

• Enterprise systems should ignore social concerns to focus on efficiency.

• Enterprise systems must consider privacy laws, data misuse, and public concerns.

• Enterprise systems can operate without considering ethical implications.